Quality improvement of adaptive optics retinal images using conditional adversarial networks

The adaptive optics (AO) technique is widely used to compensate for ocular aberrations and improve imaging resolution. However, when affected by intraocular scatter, speckle noise, and other factors, the quality of the retinal image will be degraded. To effectively improve the image quality without increasing the imaging system's complexity, the post-processing method of image deblurring is adopted. In this study, we proposed a conditional adversarial network-based method for directly learning an end-to-end mapping between blurry and restored AO retinal images. The proposed model was validated on synthetically generated AO retinal images and real retinal images. The restoration results of synthetic images were evaluated with the metrics of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), perceptual distance, and error rate of cone counting. Moreover, the blind image quality index (BIQI) was used as the no-reference image quality assessment (NR-IQA) algorithm to evaluate the restoration results on real AO retinal images. The experimental results indicate that the images restored by the proposed method have sharper quality and higher signal-to-noise ratio (SNR) when compared with other state-of-the-art methods, which has great practical significance for clinical research and analysis.